@article{ART003152816},
author={Jung-Hoon Kim and Hye-Won Yoon and Seung-Won Yoon and Da-Hyun Jang and Tae-Won Park and Jun-Won Lee and Kyu-Chul Lee},
title={Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={12},
pages={41-49}
TY - JOUR
AU - Jung-Hoon Kim
AU - Hye-Won Yoon
AU - Seung-Won Yoon
AU - Da-Hyun Jang
AU - Tae-Won Park
AU - Jun-Won Lee
AU - Kyu-Chul Lee
TI - Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 12
PB - The Korean Society Of Computer And Information
SP - 41
EP - 49
SN - 1598-849X
AB - Urban Air Mobility (UAM) has garnered significant attention as a sustainable and efficient alternative for urban transportation. However, ensuring safe operations in complex urban environments necessitates research aimed at improving the accuracy and efficiency of flight trajectory prediction. This study addresses this need by proposing a deep learning-based trajectory prediction model that overcomes the limitations of conventional machine learning and simple regression models. Using data generated directly by the Korean Air Aerospace Technology Research Institute, the study conducts a comparative analysis of Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models, while optimizing hyperparameters to maximize model performance. The experimental results demonstrate that the GRU model achieved the lowest RMSE and shortest inference time, making it the most suitable for real-time UAM trajectory prediction systems. Additionally, validation experiments using new, unseen data further confirmed the practical applicability of the GRU model. This study not only evaluates the performance of trajectory prediction models based on UAM latitude, longitude, and altitude data but also proposes a practical framework capable of real-time trajectory prediction in urban environments. Through these contributions, this research aims to enhance the safety and efficiency of UAM operations and establish a technical foundation for developing a new paradigm in urban transportation.
KW - Urban Air Mobility (UAM);Trajectory prediction;Deep Learning;GRU (Gated Recurrent Unit);Time Series prediction
DO -
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Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee and Kyu-Chul Lee. (2024). Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility. Journal of The Korea Society of Computer and Information, 29(12), 41-49.
Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee and Kyu-Chul Lee. 2024, "Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility", Journal of The Korea Society of Computer and Information, vol.29, no.12 pp.41-49.
Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee, Kyu-Chul Lee "Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility" Journal of The Korea Society of Computer and Information 29.12 pp.41-49 (2024) : 41.
Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee, Kyu-Chul Lee. Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility. 2024; 29(12), 41-49.
Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee and Kyu-Chul Lee. "Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility" Journal of The Korea Society of Computer and Information 29, no.12 (2024) : 41-49.
Jung-Hoon Kim; Hye-Won Yoon; Seung-Won Yoon; Da-Hyun Jang; Tae-Won Park; Jun-Won Lee; Kyu-Chul Lee. Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility. Journal of The Korea Society of Computer and Information, 29(12), 41-49.
Jung-Hoon Kim; Hye-Won Yoon; Seung-Won Yoon; Da-Hyun Jang; Tae-Won Park; Jun-Won Lee; Kyu-Chul Lee. Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility. Journal of The Korea Society of Computer and Information. 2024; 29(12) 41-49.
Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee, Kyu-Chul Lee. Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility. 2024; 29(12), 41-49.
Jung-Hoon Kim, Hye-Won Yoon, Seung-Won Yoon, Da-Hyun Jang, Tae-Won Park, Jun-Won Lee and Kyu-Chul Lee. "Comparative Analysis of Deep Learning Models for Trajectory Prediction in Urban Air Mobility" Journal of The Korea Society of Computer and Information 29, no.12 (2024) : 41-49.
